An adaptive improved unsaturated bistable stochastic resonance method based on weak signal detection

Abstract

Stochastic resonance can detect weak periodic signals from strong background noise without loss of signal energy. However, the classical bistable stochastic resonance has the inherent output saturation defect, which limits the detection performance of system. And it is more difficult to detect signal with strong background noise. In this article, we constructed improved unsaturated bistable stochastic resonance to overcome this shortcoming. The improved bistable potential function makes the output signal more easily oscillate in two potential wells. To improve the stability and the accuracy of the method, we further propose an adaptive improved unsaturated bistable stochastic resonance (AIUBSR) by constructing a synthetic index (SI). The SI combines zero‐crossing ratio and structural correlation coefficient, which can measure the periodicity of output signal and the accuracy of detective frequency at the same time. Theoretical analysis and numerical simulations show that the proposed AIUBSR can have good weak signal detection capability in strong background noise.

Extracting weak electromagnetic signals based on RMPSO-SPBSR in virtual anechoic chamber

“…The output SNR can measure the performance of SNR, calculated using Kramer's ratio [29]. Kramer's evaluation of CBSR can be expressed as:…”

“…Tang and Shi [28] introduced an asymmetric secondorder SR method that optimizes the SR state by adjusting the damping factor and asymmetry. Cui et al [29] proposed an improved unsaturated bistable SR system that utilizes the synthetic index (SI) for adaptive adjustment to overcome the output saturation issue in classical bistable sr (CBSR) systems, which exhibits good weak signal detection capability in highnoise conditions. But it is less effective for high-frequency signals.…”

Compound faults diagnosis of rotating machinery via enhanced two-layer sliding correlated kurtosis

“…Much research efforts have been contributed in decades to detect the damage feature from vibration measurements, and various signal denoising or analyzing techniques have been reported in which the single fault detection is greatly focused. These achievements mainly contain filtering methods such as wavelet transforms (WTs) (Kavitha et al, 2022), wavelet package transform (WPT) (Yan et al, 2014), spectral kurtosis (Fu et al, 2021), and flexible-frame wavelet transforms (Zhang et al, 2015, 2020; Cao et al, 2019); adaptive vibration signal decomposition methods such as empirical mode decomposition (EMD) (Zheng et al, 2022), local mean decomposition (LMD) (Chen et al, 2022), variable mode decomposition (VMD) (Fan et al, 2022), and ensemble empirical mode decomposition (EEMD) (Hsu and Huang, 2022); feature enhancement methods such as stochastic resonance (Lu et al, 2017) with nonlinear bistable oscillators (Cui et al, 2021), and sparse decomposition (Li et al, 2020; Wang et al, 2018); and intelligent classification methods such as machine learning (Mahami et al, 2022) and deep learning (Hou et al, 2022; Guo et al, 2018). Most of these methods have been applied for analyzing simulation and experimental vibration signals, and some of these aforementioned methods are reported suitable for incipient fault diagnosis (Jiang et al, 2016).…”

Chaotic Effect-Based Array Duffing Systems with Improved Nonlinear Restoring Force for Weak Signal Detection in Dynamic MWD

“…In recent years, the profound study for nonlinear science has provided a new way for the researcher to understand, analyze and solve the problem of weak signal detection. The discovery of nonlinear dynamic phenomena such as chaos, fractal and stochastic resonance poses a powerful challenge to traditional signal processing methods [23][24][25][26]. Applying chaos theory to signal detection is a new research field rising from the 1990s.…”